Benchmarking the Effectiveness and Accuracy of Multiple Mitochondrial DNA Variant Callers: Practical Implications for Clinical Application.

Eddie K K Ip,Michael Troup,David S Winlaw,Eleni Giannoulatou,Sally L Dunwoodie,Colin Xu

doi:10.3389/fgene.2022.692257

Abstract

Mitochondrial DNA (mtDNA) mutations contribute to human disease across a range of severity, from rare, highly penetrant mutations causal for monogenic disorders to mutations with milder contributions to phenotypes. mtDNA variation can exist in all copies of mtDNA or in a percentage of mtDNA copies and can be detected with levels as low as 1%. The large number of copies of mtDNA and the possibility of multiple alternative alleles at the same DNA nucleotide position make the task of identifying allelic variation in mtDNA very challenging. In recent years, specialized variant calling algorithms have been developed that are tailored to identify mtDNA variation from whole-genome sequencing (WGS) data. However, very few studies have systematically evaluated and compared these methods for the detection of both homoplasmy and heteroplasmy. A publicly available synthetic gold standard dataset was used to assess four mtDNA variant callers (Mutserve, mitoCaller, MitoSeek, and MToolBox), and the commonly used Genome Analysis Toolkit “best practices” pipeline, which is included in most current WGS pipelines. We also used WGS data from 126 trios and calculated the percentage of maternally inherited variants as a metric of calling accuracy, especially for homoplasmic variants. We additionally compared multiple pathogenicity prediction resources for mtDNA variants. Although the accuracy of homoplasmic variant detection was high for the majority of the callers with high concordance across callers, we found a very low concordance rate between mtDNA variant callers for heteroplasmic variants ranging from 2.8% to 3.6%, for heteroplasmy thresholds of 5% and 1%. Overall, Mutserve showed the best performance using the synthetic benchmark dataset. The analysis of mtDNA pathogenicity resources also showed low concordance in prediction results. We have shown that while homoplasmic variant calling is consistent between callers, there remains a significant discrepancy in heteroplasmic variant calling. We found that resources like population frequency databases and pathogenicity predictors are now available for variant annotation but still need refinement and improvement. With its peculiarities, the mitochondria require special considerations, and we advocate that caution needs to be taken when analyzing mtDNA data from WGS data.

Highlights

Mitochondrial DNA is only inherited from the mother because the mitochondria from the sperm cell are usually destroyed by the egg shortly after fertilization; a phenomenon known as a matrilineal inheritance (Sutovsky et al, 1999)
The genetic variation in Mitochondrial DNA (mtDNA) is classified into two categories: 1) homoplasmic variants, which occur when an alternative allele appears in all copies of the mtDNA genome, and are expected to be inherited from the mother, and 2) heteroplasmic variants, which occur when an alternative allele is only present in some copies of the mtDNA genome (Taylor and Turnbull 2005; Stewart and Chinnery 2015)
Using a synthetic gold-standard dataset constructed by the mixture of two mtDNA genomes (Fazzini et al, 2021), we assessed the accuracy of five mtDNA variant callers (Table 1)

Summary

Introduction

The mitochondrion is an organelle in eukaryotic cells responsible for manufacturing most of the cell’s energy. It possesses its own double-stranded circular genome of 16,569 nucleotides, which encodes for the 12S and 16S rRNAs, 22 tRNAs, and 13 polypeptides (Anderson et al, 1981; Taanman 1999). The genetic variation in mtDNA is classified into two categories: 1) homoplasmic variants, which occur when an alternative allele appears in all copies of the mtDNA genome, and are expected to be inherited from the mother, and 2) heteroplasmic variants, which occur when an alternative allele is only present in some copies of the mtDNA genome (Taylor and Turnbull 2005; Stewart and Chinnery 2015). The presence of repeats when short read sequencing data are used adds an additional challenge to this task

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